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Semikong

Semikong is an open-source semiconductor AI project combining a domain-specific language model with a semiconductor ontology and knowledge graph. Its repository supports model training and inference experiments, alongside semantic modeling, validation, provenance and shared terminology for structured semiconductor data.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Semikong — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Semikong when a semiconductor-focused model, ontology or knowledge graph is more relevant than a general-purpose AI stack, especially for work spanning process, equipment, materials, fabrication, packaging, testing or supply-chain knowledge.

Who it is for

  • AI engineers exploring semiconductor-specific language models
  • Semiconductor researchers and technical knowledge teams
  • Data and knowledge-graph specialists
  • Builders developing domain-specific document or question-answering workflows

Documented uses

  • Train or run inference experiments with a semiconductor language model.
  • Structure semiconductor concepts and data through an ontology and knowledge graph.
  • Develop grounded semiconductor question-answering, document analysis or generation workflows.
  • Organize provenance-aware semiconductor information for machine and human use.
Before you choose

Requirements & limits

The public repository is accessible and marked open source. It is implemented in Python, supports self-hosted or server use, and is distributed under the MIT License. Some model weights, datasets and imported ontology assets may have separate upstream terms.

  • The repository separates model work from ontology work, so the relevant component can be adopted according to the task.
  • Combining model and ontology layers is identified as a supported route to grounded semiconductor AI workflows.
  • The ontology materials provide structure for terminology, validation and provenance rather than only text generation.
  • The repository does not specify a production deployment architecture in the supplied material.
  • Asset-level licensing and provenance may differ from the repository’s MIT License.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the Semikong model workspace

Install the model component and use its documented training and inference entry points.

Before you start

  • A local or server environment suitable for the Python repository.
  • Access to the public Semikong repository.
  • The model documentation and Makefile in model/.

Steps

  1. Open the Semikong repository and review model/README.md, model/INSTALL.md and model/USAGE.md.

  2. Run the repository’s documented model installation target with the command below.

    Documented command
    make -C model install

  3. Use the documented training target with the command below when preparing a training experiment.

    Documented command
    make -C model train

  4. Use the documented inference target with the command below for an inference experiment.

    Documented command
    make -C model infer

Expected outcome

A model workspace prepared around the repository’s documented installation, training and inference entry points.

Guide limitations

  • The supplied repository material does not specify hardware requirements, dataset preparation, expected runtime, output quality or production deployment architecture.
  • Model weights, datasets and imported assets may have upstream licensing or provenance-specific terms.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
SemikongTrain or run inference experiments with a semiconductor language model.Self-hosted, ServerThe public repository is accessible and marked open source. It is implemented in Python, supports self-hosted or server use, and is distributed under the MIT License. Some model weights, datasets and imported ontology assets may have separate upstream terms.
Pulse Of MotionEstimate a video's physical frame rate from its visual dynamics.LinuxThe repository is public. Its README describes a released checkpoint that downloads from Hugging Face on first inference, with a manual download option. The strongest internal model is not fully released; the README directs interested users to contact the project about access. No license information is provided in the repository evidence.
Neural Image AssessmentTrain the model using AVA images and rating annotations.Cloud, Linux, macOS, Windows
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
4 October 2026
Original website title
aitomatic/semikong · Official homepage title · retained in its source language
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